demos/TTSArmLinux: adjust the code style to match the project

demos/TTSArmLinux/src/Predictor.hpp

@@ -10,23 +10,17 @@
 using namespace paddle::lite_api;
 
 class Predictor {
-private:
-    float inferenceTime = 0;
-    std::shared_ptr<PaddlePredictor> AMPredictor = nullptr;
-    std::shared_ptr<PaddlePredictor> VOCPredictor = nullptr;
-    std::vector<float> wav;
-
 public:
-    bool init(const std::string &AMModelPath, const std::string &VOCModelPath, int cpuThreadNum, const std::string &cpuPowerMode) {
+    bool Init(const std::string &AMModelPath, const std::string &VOCModelPath, int cpuThreadNum, const std::string &cpuPowerMode) {
         // Release model if exists
-        releaseModel();
+        ReleaseModel();
 
-        AMPredictor = loadModel(AMModelPath, cpuThreadNum, cpuPowerMode);
+        AM_predictor_ = LoadModel(AMModelPath, cpuThreadNum, cpuPowerMode);
-        if (AMPredictor == nullptr) {
+        if (AM_predictor_ == nullptr) {
             return false;
         }
-        VOCPredictor = loadModel(VOCModelPath, cpuThreadNum, cpuPowerMode);
+        VOC_predictor_ = LoadModel(VOCModelPath, cpuThreadNum, cpuPowerMode);
-        if (VOCPredictor == nullptr) {
+        if (VOC_predictor_ == nullptr) {
             return false;
         }
 
@@ -34,11 +28,11 @@ public:
     }
 
     ~Predictor() {
-        releaseModel();
+        ReleaseModel();
-        releaseWav();
+        ReleaseWav();
     }
 
-    std::shared_ptr<PaddlePredictor> loadModel(const std::string &modelPath, int cpuThreadNum, const std::string &cpuPowerMode) {
+    std::shared_ptr<PaddlePredictor> LoadModel(const std::string &modelPath, int cpuThreadNum, const std::string &cpuPowerMode) {
         if (modelPath.empty()) {
             return nullptr;
         }
@@ -68,13 +62,13 @@ public:
         return CreatePaddlePredictor<MobileConfig>(config);
     }
 
-    void releaseModel() {
+    void ReleaseModel() {
-        AMPredictor = nullptr;
+        AM_predictor_ = nullptr;
-        VOCPredictor = nullptr;
+        VOC_predictor_ = nullptr;
     }
 
-    bool runModel(const std::vector<float> &phones) {
+    bool RunModel(const std::vector<float> &phones) {
-        if (!isLoaded()) {
+        if (!IsLoaded()) {
             return false;
         }
 
@@ -82,26 +76,26 @@ public:
         auto start = std::chrono::system_clock::now();
 
         // 执行推理
-        VOCOutputToWav(getAMOutput(phones));
+        VOCOutputToWav(GetAMOutput(phones));
 
         // 计时结束
         auto end = std::chrono::system_clock::now();
 
         // 计算用时
         std::chrono::duration<float> duration = end - start;
-        inferenceTime = duration.count() * 1000; // 单位：毫秒
+        inference_time_ = duration.count() * 1000; // 单位：毫秒
 
         return true;
     }
 
-    std::unique_ptr<const Tensor> getAMOutput(const std::vector<float> &phones) {
+    std::unique_ptr<const Tensor> GetAMOutput(const std::vector<float> &phones) {
-        auto phones_handle = AMPredictor->GetInput(0);
+        auto phones_handle = AM_predictor_->GetInput(0);
         phones_handle->Resize({static_cast<int64_t>(phones.size())});
         phones_handle->CopyFromCpu(phones.data());
-        AMPredictor->Run();
+        AM_predictor_->Run();
 
         // 获取输出Tensor
-        auto am_output_handle = AMPredictor->GetOutput(0);
+        auto am_output_handle = AM_predictor_->GetOutput(0);
         // 打印输出Tensor的shape
         std::cout << "AM Output shape: ";
         auto shape = am_output_handle->shape();
@@ -116,16 +110,16 @@ public:
     }
 
     void VOCOutputToWav(std::unique_ptr<const Tensor> &&input) {
-        auto mel_handle = VOCPredictor->GetInput(0);
+        auto mel_handle = VOC_predictor_->GetInput(0);
         // [?, 80]
         auto dims = input->shape();
         mel_handle->Resize(dims);
         auto am_output_data = input->mutable_data<float>();
         mel_handle->CopyFromCpu(am_output_data);
-        VOCPredictor->Run();
+        VOC_predictor_->Run();
 
         // 获取输出Tensor
-        auto voc_output_handle = VOCPredictor->GetOutput(0);
+        auto voc_output_handle = VOC_predictor_->GetOutput(0);
         // 打印输出Tensor的shape
         std::cout << "VOC Output shape: ";
         auto shape = voc_output_handle->shape();
@@ -139,25 +133,25 @@ public:
         for (auto dim : voc_output_handle->shape()) {
             output_size *= dim;
         }
-        wav.resize(output_size);
+        wav_.resize(output_size);
         auto output_data = voc_output_handle->mutable_data<float>();
-        std::copy_n(output_data, output_size, wav.data());
+        std::copy_n(output_data, output_size, wav_.data());
     }
 
-    bool isLoaded() {
+    bool IsLoaded() {
-        return AMPredictor != nullptr && VOCPredictor != nullptr;
+        return AM_predictor_ != nullptr && VOC_predictor_ != nullptr;
     }
 
-    float getInferenceTime() {
+    float GetInferenceTime() {
-        return inferenceTime;
+        return inference_time_;
     }
 
-    const std::vector<float> & getWav() {
+    const std::vector<float> & GetWav() {
-        return wav;
+        return wav_;
     }
 
-    void releaseWav() {
+    void ReleaseWav() {
-        wav.clear();
+        wav_.clear();
     }
 
     struct WavHeader {
@@ -185,7 +179,7 @@ public:
         uint32_t data_size = 0;
     };
 
-    bool writeWavToFile(const std::string &wavPath) {
+    bool WriteWavToFile(const std::string &wavPath) {
         std::ofstream fout(wavPath, std::ios::binary);
         if (!fout.is_open()) {
             return false;
@@ -194,15 +188,21 @@ public:
         // 写入头信息
         WavHeader header;
         header.size = sizeof(header) - 8;
-        header.data_size = wav.size() * sizeof(float);
+        header.data_size = wav_.size() * sizeof(float);
         header.byte_rate = header.sample_rate * header.num_channels * header.bits_per_sample / 8;
         header.block_align = header.num_channels * header.bits_per_sample / 8;
         fout.write(reinterpret_cast<const char*>(&header), sizeof(header));
 
         // 写入wav数据
-        fout.write(reinterpret_cast<const char*>(wav.data()), header.data_size);
+        fout.write(reinterpret_cast<const char*>(wav_.data()), header.data_size);
 
         fout.close();
         return true;
     }
+
+private:
+    float inference_time_ = 0;
+    std::shared_ptr<PaddlePredictor> AM_predictor_ = nullptr;
+    std::shared_ptr<PaddlePredictor> VOC_predictor_ = nullptr;
+    std::vector<float> wav_;
 };

demos/TTSArmLinux/src/main.cc

@@ -50,19 +50,19 @@ int main(int argc, char *argv[]) {
     }
 
     Predictor predictor;
-    if (!predictor.init(AMModelPath, VOCModelPath, 1, "LITE_POWER_HIGH")) {
+    if (!predictor.Init(AMModelPath, VOCModelPath, 1, "LITE_POWER_HIGH")) {
         std::cerr << "predictor init failed" << std::endl;
         return -1;
     }
     
-    if (!predictor.runModel(sentencesToChoose[sentencesIndex])) {
+    if (!predictor.RunModel(sentencesToChoose[sentencesIndex])) {
         std::cerr << "predictor run model failed" << std::endl;
         return -1;
     }
 
-    std::cout << "Inference time: " << predictor.getInferenceTime() << "ms, WAV size: " << predictor.getWav().size() << std::endl;
+    std::cout << "Inference time: " << predictor.GetInferenceTime() << "ms, WAV size: " << predictor.GetWav().size() << std::endl;
 
-    if (!predictor.writeWavToFile(outputWavPath)) {
+    if (!predictor.WriteWavToFile(outputWavPath)) {
         std::cerr << "write wav file failed" << std::endl;
         return -1;
     }